Decisional Needs of African, Caribbean, and Black Patients Diagnosed with Brain-Heart Conditions
Bibliographic record
Abstract
Equity-deserving groups, including African, Caribbean, and Black (ACB) populations, face barriers to equitable brain-heart healthcare. These barriers contribute to unmet decisional needs and challenges in making informed health decisions. For my master's thesis, I aimed to investigate the decisional needs of ACB patients with brain-heart conditions and the unique challenges they encounter during decision-making using an explanatory sequential mixed-methods design. We included participants from equity-deserving groups who participated in an ongoing parent study. We administered surveys and conducted semi-structured interviews with adult patients from the Ottawa Hospital, the University of Ottawa Heart Institute, and community organizations. Our work was guided by the Ottawa Decision Support Framework, PROGRESS-Plus framework, and intersectionality theory. Survey results from 23 participants facing a variety of brain-heart health decisions in the past 12 months revealed that seven (30.4%) participants experienced clinically significant decisional conflict and six (30.0%) experienced clinically significant decision regret. The common challenges that participants experienced during decision-making included worrying about choosing the wrong option (n=10, 50%) and feeling that brain implications were never part of the conversation for their heart condition (n=9, 45%). The interviews further demonstrated complex barriers contributing to their unmet decisional needs, such as difficulties in accessing health information, strong emotions, challenges with patient-clinician communication, mistrust, and barriers to healthcare access. We integrated these findings using joint displays. The insights gained from this study can inform the development of equitable decision-support interventions to effectively address the decisional needs of ACB patients with brain-heart conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".